|
''' |
|
Author: Egrt |
|
Date: 2022-03-19 10:23:48 |
|
LastEditors: Egrt |
|
LastEditTime: 2022-03-21 00:05:27 |
|
FilePath: \Luuu\app.py |
|
''' |
|
|
|
from gis import GIS |
|
import gradio as gr |
|
import os |
|
|
|
|
|
os.system('pip install requirements.txt') |
|
from zipfile import ZipFile |
|
gis = GIS() |
|
|
|
|
|
|
|
def inference(filepath): |
|
filename, file_list = gis.detect_image(filepath) |
|
with ZipFile("result.zip", "w") as zipObj: |
|
zipObj.write(file_list[0], "{}.tif".format(filename+'mask')) |
|
zipObj.write(file_list[1], "{}.tif".format(filename)) |
|
zipObj.write(file_list[2], "{}.pdf".format(filename)) |
|
zipObj.write(file_list[3], "{}.cpg".format(filename)) |
|
zipObj.write(file_list[4], "{}.dbf".format(filename)) |
|
zipObj.write(file_list[5], "{}.shx".format(filename)) |
|
zipObj.write(file_list[6], "{}.shp".format(filename)) |
|
zipObj.write(file_list[7], "{}.prj".format(filename)) |
|
return "result.zip" |
|
|
|
|
|
|
|
title = "基于帧场学习的多边形建筑提取" |
|
description = "目前最先进图像分割模型通常以栅格形式输出分割，但地理信息系统中的应用通常需要矢量多边形。我们在遥感图像中提取建筑物的任务中，将帧场输出添加到深度分割模型中，将预测的帧场与地面实况轮廓对齐，帮助减少深度网络输出与下游任务中输出样式之间的差距。 @Luuuu🐋🐋" |
|
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.14875' target='_blank'>Polygonization-by-Frame-Field-Learning</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>" |
|
example_img_dir = 'images' |
|
example_img_name = os.listdir(example_img_dir) |
|
examples=[[os.path.join(example_img_dir, image_path)] for image_path in example_img_name if image_path.endswith('.png')] |
|
gr.Interface( |
|
inference, |
|
[gr.inputs.Image(type="filepath", label="待检测图片")], |
|
gr.outputs.File(label="检测结果"), |
|
title=title, |
|
description=description, |
|
article=article, |
|
enable_queue=True, |
|
examples=examples |
|
).launch(debug=True) |
